There is no denying the importance of consumer lending. Not only is it one of the fastest growing segments of the banking portfolio, but it also provides some of the highest spreads and yields to the industry.
Although this sounds encouraging, there are some disturbing trends leading many to suggest that consumer lending could be the next credit crisis.
Consumer indebtedness is increasing. In the fourth quarter of 1996, consumer credit outstanding rose by $43 billion, to $1.226 trillion, according to the Federal Reserve.
Bankruptcies, delinquencies, and chargeoffs are all increasing, and the consumer is more highly leveraged today than any time in history. Taken together, these factors could be a recipe for disaster.
How management analyzes and understands the trade-offs between credit risk and the profitability of consumer portfolios will determine the winners and the losers.
It seems every bank's management believes that it is doing a good job and that its competitors are assuming risk unknowingly or with reckless abandon. The key is how management actually analyzes the credit risk and profitability of its portfolios.
We believe lenders should focus on three major areas: portfolio segmentation, scoring systems, and macroeconomics.
Portfolio segmentation and scoring systems are well understood by most institutions even though the effectiveness of their application varies widely. We will focus on using macroeconomics to anticipate trends, modify tactics, and influence strategy to better manage credit risk and profitability.
Consumer portfolio managers have always intuitively considered macroeconomics as part of their managerial decision process. Recently, there has been research concerning the use of macroeconomics to quantitatively predict trends in consumer lending, such as delinquencies, bankruptcies, and chargeoffs.
This is a developing area that will be a powerful tool in the future and a key differentiator between those managers who are able to seize opportunities in consumer lending and those who flirt with disaster.
The theory is simple. If consumer interest rates rise, the burden on consumers increases and delinquencies should also increase. If consumer indebtedness as a percentage of disposable income increases, credit quality should decrease. If unemployment rises, delinquencies should rise, and so on.
But determining which indicators have the greatest impact on delinquencies, bankruptcies, or chargeoffs is much more complex. Through the use of sophisticated multiple regression analysis, we are able to identify economic factors that will be predictive for various portfolio segments.
This way, an institution can evaluate which segments of their portfolio have the greatest credit risk under current and future economic conditions.
In addition, we know there is a significant lag between the time an economic indicator changes and the resulting delinquencies, chargeoffs, or bankruptcies occur.
Therefore, using economic indicators with regression analysis can give warning signals to potential problems or opportunities in a portfolio. This technique allows an institution to identify which portfolio segments have the greatest profitability potential and the least credit risk and to evaluate various tactical and strategic decisions to enhance overall long- term profitability.
For one thing, the risk relationship between different groups predicted by credit scoring models may not be constant over time.
One limitation of credit scoring models is their development of relative risk relationships using past history, holding constant other factors that affect both groups similarly. Are these "other factors" important from a lender's perspective? If they are, what is their impact on consumer credit delinquency?
Delinquency rates rose from 2.3% in the second half of 1981 to 5.4% at yearend 1996. More recently, the ratio of accounts past due to payments and the dollar amounts delinquent have both gone up.
Clearly, the monthly fluctuations in delinquency rates show that borrower-specific credit scores alone do not provide sufficient information for managing a consumer loan portfolio. The "other factors" held constant in credit scoring models have important implications for the profitability of those consumer loans.
The fluctuations in consumer delinquency rates across time are related to certain macroeconomic factors. The key is to identify the macroeconomic factors useful in predicting future changes in consumer delinquencies, rather than those that occur simultaneously or after a change in delinquencies. It is leading macroeconomic indicators that are key.
One such indicator is business failures. The average annual percentage changes in both liabilities of failed businesses and credit card delinquencies have moved together in the last six years. The figure suggests that these two variables move together at the same time. In fact, a change in business failures typically precedes a change in the delinquency rate.
More than just a positive correlation exists between business failures and credit card delinquencies; a causation relationship exists. When analyzed using monthly data, the change in liabilities of failed businesses acts as a leading economic indicator or predictor of delinquency trends.
In contrast, personal bankruptcies have a lagged, not a leading, relationship with delinquency trends. Credit card delinquency rates act as a leading indicator for personal bankruptcies.
Consumer loan delinquency trends can be forecast based on the movement of leading macroeconomic indicators. The leading macroeconomic variables are the tides that can raise or lower a portfolio's performance, and may also alter the risk relationships estimated by the credit scoring models.
Therefore, a combination of credit scoring and macroeconomic analyses can be a cutting-edge tool that empowers consumer lenders to better manage a portfolio.
Based on discussions with many financial institutions, we have found that they are taking advantage of the extensive information about consumer credit histories through credit scoring models. These models have resulted in significant gains for consumer lenders.
Consumer lenders, however, have not focused on macroeconomic factors that affect total delinquency trends as well as relative risk scores. Future efforts investigating macroeconomic effects on consumer delinquency rates may be as important as further refinements to credit scoring models.
Most financial institutions use migration analysis to predict future delinquency and chargeoff experience for a given portfolio of loans. A percentage of loans that are 30 days past due will become 60 days past due in the next month, and so on. The migration analysis is based on past historical experience of the institution, but generally is assumed to remain constant in the future.
Similar to consumer credit card delinquency rates, migration analysis typically does not factor in changing macroeconomic conditions. In actual practice, the migration pattern of delinquencies to defaults is likely to accelerate in bad economic periods and slow down in good economic periods.
Forecasting with a combination of historical migration analysis and macroeconomic analysis is likely to improve future predictions of loan delinquencies and defaults.
Incorporating macroeconomic forecasts into an institution's credit scoring model and migration analysis can enhance the ability of consumer lenders to predict future delinquencies and losses.
The use of statistical economic regression can base future forecasts on empirical information in combination with the statistical approaches of credit scoring and migration analysis, rather than relying on anecdotes and hunches.
The following are some of the tactical choices of an institution when utilizing economic indicators: Change portfolio allocations; increase scoring system thresholds for portfolios subject to higher risk of credit loss; lower scoring system thresholds for portfolios subject to lower credit risk loss;increase/decrease size of collection staffs in anticipation of increased/decreased delinquencies.
As institutions gain confidence and improve their techniques of using economic indicators as a normal part of their management information, they will be better able to make strategic decisions based upon anticipated long-term economic trends.
Strategic decisions could include: Shifting portfolio allocations between various types of consumer debt; focusing credits in a particular region of the country or a unique demographic portfolio segment; reducing exposure to long-term credit risk by securitizing or selling certain portfolio segments.
Clearly, the use of multiple regression analysis and economic indicators to predict delinquencies, chargeoffs, and bankruptcies is in its infancy.
However, the logic of making this a normal part of management's information is overwhelming. The ability to adjust scoring thresholds for purposes of marketing decisions, origination decisions, and portfolio risk management purposes could be the difference between the winners and losers in the future.
